In the diagnosis and treatment of cancer and other diseases, as well in the development of new drugs and medical therapeutic procedures, it is often necessary to take a biopsy to determine if the mass is malignant or benign. Pathological examination of a lesion in the body tissue of an animal which has a natural or surgically exposed surface, usually requires that a pathologist interpret slides prepared from sections of the tissue specimen or lesion, i.e., histologically prepared sections or slices.
Preclinical evaluation of new drugs and medical care, for example in toxicology studies for a drug under development, or a material under investigation, routinely involve excision, fixation and slicing of target organs for pathology assessment. These organs, e.g., liver, heart, kidneys, gut, brain of laboratory animals, such as mice and rats are examined by histology to identify various pathologies, such as tumors, ischemic lesions, deformations, inflammation, hyperplasia, fibrosis, etc. In the course of each preclinical trial, a dozen, and hundreds, even thousands of organs are examined.
As is underlined in Zaviskan, US patent application 20020010394, borders of the slices are referred to as margins and may contain diseased or healthy tissue. After suitable processing, the tissue specimen or slices thereof are embedded or otherwise fixated e.g., in paraffin blocks or by frizzing unfixed specimens. Histological sections are then cut from the tissue slices with a microtome and stained for microscopic examination and interpretation by a pathologist. It is generally required that the histologically prepared sections from the tissue specimen represent a common suite or set of sections selected to provide information to diagnose the type of pathologic lesion and its extent. This suite of sections may generally include at least one section along the major axis of the tissue ellipse (i.e., along the length of the ellipse), at least one to two sections on each side of the tissue ellipse transversing the major axis, and at least three to four sections from the center of the lesion. The number of slices in the suite increases with the size of the lesion, its condition before and after cutting, embedding and processing.
A WEB article “Preclinical imaging: The use of preclinical magnetic resonance imaging (MRI) for characterization of disease progression and response to therapy” indicates that MRI is been widely used in preclinical research on experimental small animals. Studies have typically been aimed at understanding the pathophysiological status and evaluating the efficacy and side effects of newly developed treatments such as pharmaceutical and regenerative medicine.
When having in hand the target organ to be sliced for pathological investigation, there are conventions and technical procedures on how many slices to obtain from the organ. In an organ of a few centimeters, when slicing a few slices of approximately 5 μm each, the lesion, even if present, can be missed. Image analysis techniques are utilized in the domain of histopathology, specifically for the objective of automated carcinoma detection and classification. CAD systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted in order to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. Hence, for example, Madabhushi et al., Automated Detection of Prostatic Adenocarcinoma From High-Resolution Ex Vivo MRI IEEE Transactions On Medical Imaging, Vol. 24, No. 12, December 2005 discloses an automated computer-aided detection (CAD) system for detecting prostatic adenocarcinoma from 4 Tesla ex vivo magnetic resonance (MR) imagery of the prostate. Madabhushi's CAD system was thus designed for enhancing visual detection of prostate cancer, and thus to overcome lack of shape, differences in texture and image intensity, and to avoid image overlapping between malignant and benign structures. Madabhushi's method for improved imageability was as follows: (a) acquiring [MR] images of the adenocarcinoma; (b) correcting for background inhomogeneity and nonstandardness; (c) extracting three-dimensional (3-D) texture features from the 3-D MRI scene; (d) assigning, by means of a Bayesian classifier, each image voxel a “likelihood” of malignancy for each feature independently; (e) combining said “likelihood” images using an optimally weighted feature combination scheme; (f) quantitatively evaluating by comparing the CAD results with the manually ascertained ground truth for the tumor on the MRI; and then (g) visually registering the MR slices with the corresponding regions on the histology slices thereby manually determining tumor labels on the MR slices by an expert.
Alic et al., see: Facilitating Tumor Functional Assessment by Spatially Relating 3D Tumor Histology and In Vivo MRI: Image Registration Approach. PLoS ONE 6(8): e22835. doi:10.1371/journal.pone.0022835; 2011, shows that established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.
McGrath et al., in Fiducial Markers for Correlation of Whole-Specimen Histopathology with MR Imaging at 7 Tesla. Med Phys. 2010 May; 37(5):2321-8, shows registration of 3D histopathology with 3D in vivo imaging validated tumor boundary delineation for targeted radiation cancer therapy. They have also shown that accurate correlation is compromised by tissue distortion induced by histopathological processing.
U.S. Pat. No. 8,189,737 discloses a processes for producing a microCT image for virtual histology using x-ray microscopic computed tomography along with processes for rapid and inexpensive high-throughput methods of high resolution imaging for screening an ex vivo embryo for phenotype using computed tomography imaging.
It is thus a long felt need to obtain either a CAD-based or manually guided slicing system for obtaining histological specimens and guided slicing methods; decreasing the chance of misdiagnosis due to missing the right slice in the sample and increasing by that the “likelihood” of detecting malignancy or any other pathology.